Guido Fioretti

The organizational learning curve

Abstract A very practical consequence of organizational learning is that the time required to produce a unit decreases with the total number of units produced. Potentially, this thumb rule known as the learning curve has a great practical importance. Unfortunately, no procedure is available to predict the pace and extent at which the production time will decrease. Aggregate models are able to fit empirical data with a few parameters, but they are unable to link these parameters to specific properties of an organization. This article links the parameters of the only available disaggregate model of the learning curve to measurable features of the component units of an organization. Unfortunately, no data are available that may confirm that precisely these features are key to organizational learning. However, analytical results derived under simplifying assumptions yield meaningful dynamics that appear to fit with empirical reports. This circumstance suggests that practical applications will be possible in the future.

A Subjective Measure Of Complexity

This article presents a quantitative measure of complexity, subjectively understood as a property of the relationship between a system and its observer instead of as a property of the system itself. Within this framework, complexity is quantified by assuming to know the mental categories and the mental model by which a system is represented in the mind of its observer. It is argued that this subjectivist concept of complexity is not in contrast with objective measures of complexity introduced in particular domains, but generalizes complexity to domains where no objective measure is feasible. An extensive numerical example is presented and thoroughly discussed.

The garbage can model of organizational choice: An agent-based reconstruction

Abstract We reconstruct Cohen, March and Olsen’s original Garbage Can model of organizational choice as a multi-agent system. We show that while some of the original results might be viewed as artifacts of particular modeling choices, the basic insights of the original model are confirmed over a wide range of experimental conditions. The simulation experiments confirm and extend some of the most interesting conclusions of the Garbage Can model: most decisions are made without solving any problem, the few problems that are solved generally pertain to the lower hierarchical levels and, consequently, the members of an organization encounter the same old problems again and again.

A Cognitive Approach to Organizational Complexity

Organizational theory has construed complexity as an objective characteristic of either the structure or the behaviour of an organization. We argue that to further our understanding it should be understood in terms of human cognition of a structure or behavior. This cognitive twist is illustrated using two theoretical approaches, whose relationship is discussed.

A Mathematical Theory of Evidence for G.L.S. Shackle

Evidence Theory is a branch of mathematics that concerns combination of empirical evidence in an individual’s mind in order to construct a coherent picture of reality. Designed to deal with unexpected empirical evidence suggesting new possibilities, evidence theory is compatible with Shackle’s idea of decision-making as a creative act. This essay investigates this connection in detail, pointing to the usefulness of evidence theory to formalise and extend Shackle’s decision theory.In order to ease a proper framing of the issues involved, evidence theory is compared with sub-additive probability theory and Ewens’s infinite alleles model. Furthermore, the original version of evidence theory is presented along with its most recent developments.

Von Kries and the other : non-numerical probabilities before Keynes

Keyness A Treatise on Probability (Keynes, 1921) contains some quite unusual concepts, such as non-numerical probabilities and the ‘weights of the arguments’ that support probability judgements. Their controversial interpretation gave rise to a huge literature about ‘what Keynes really did mean’, also because Keyness later views in macroeconomics ultimately rest on his ideas on uncertainty and expectations formation.

A connectionist model of the organizational learning curve

Organizational learning can be understood as a spontaneous development of routines. Mathematically, this process can be described as a search for better paths on a graph whose nodes are humans and machines. Since the rules for connecting nodes depend on their ability to process goods, the slope of the learning curve may be connected to physical and psychological properties. Two suggestive examples are discussed.

The investment acceleration principle revisited by means of a neural network

The investment acceleration principle is a heuristic for modelling a investment time series out of a consumption time series. The model presented herein develops a disaggregated accelerator equation whose coefficients are the weights of a Kohonen neural net that represents firms’ decision-making. According to this model, investments take place when managers recognise emerging technological patterns. Furthermore, a technique borrowed from the theory of self-organising systems is used in order to disentangle innovation-driven investments from plant-replication investments.

Recognising investment opportunities at the onset of recoveries

This model focuses on the decision to invest in novel fields of activity. Making such decisions implies that managers recognize he potentialities of emerging technological patterns, which is not a trivial ability. Ultimately, it depends on the mental categories that they developed through their working life, which may or may not be appropriate for the situation they are facing. In this article, investment decision-making is modeled by means of a Kohonen neural network. Its neurons represent firms as decision-amkers, and their weights correspond to the coefficients of a disaggregated accelerator.

Either, Or: Exploration of an Emerging Decision Theory

A novel decision theory is emerging out of sparse findings in economics, mathematics and, most importantly, psychology and computational cognitive science. It rejects a fundamental assumption of the theory of rational decision making, namely, that uncertain belief rests on independent assessments of utility and probability, and includes envisioning possibilities within its scope. Several researchers working with these premises, independently of one another, have remarked that when decision is made, the positive features of the alternative that will be chosen are highlighted, and that this alternative is opposed to a loosing alternative, whose unpleasant aspects are stressed. By doing so, decision makers construct a coherent framework that provides them with a sense of direction in spite of an uncertain future. This paper frames together contributions from different disciplines, often unknown to one another, with the hope of improving the coordination of research efforts. Furthermore, it discusses the status of this emerging theory with respect to our current idea of rationality. This collection might be useful in order to develop theories and models of decision making in uncertain situations, where consequences are unknown and possibilities must be conceived. It does not provide a simple solution, but it may lay a base for future developments.